Navigational system and method for configuring a navigational system

ABSTRACT

In a navigational system for generating route recommendations and for target tracking, a system ( 500, 800 ) is proposed, which detects vehicle-, traffic-, and/or driver-related data and which from these data can derive patterns of behavior of the user. The patterns of behavior as well as the user preferences derived therefrom can then be used to apply individually adapted optimization criteria to the specific user in calculating the route. 
     When patterns of behavior are detected that deviate from the usual behavior, the detection of sensor information can automatically be expanded in order, in a plausibility check, to establish causes for the deviations and to derive therefrom new user preferences, if appropriate.

FIELD OF THE INVENTION

The present invention relates to a method for configuring a navigationalsystem, as well as to a navigational system that can suggest at leastone recommended route on the basis of predetermined behavior patterns ofthe user and on the basis of user preferences.

BACKGROUND INFORMATION

Navigational systems for supporting a traffic participant, such asespecially an automobile driver, are conventional, and they aregenerally composed of the subsystems: (a) digital road map, (b)computing module for calculating the driving route, (c) positiondetermining system, (d) system management, (e) sensors for detectingvehicle motions, (f) input unit, and (g) output unit for operationalcontrol and target tracking. The route is calculated generally on thebasis of various optimization parameters, which must be set previouslyby the user, or which are already fixedly established by themanufacturer in the algorithms for the route calculation. Theoptimization is carried out with the assistance of evaluations of theparticipating route elements contained in a database, which can include,for example, length, possible speed, type of road, and the like. Theselection of an optimal route can entail, for example, establishing thequickest or the shortest route. The particular weighting that isundertaken here can be influenced by the personal preferences of theuser.

Optimization that is preestablished by the manufacturer is usuallyparametrized to an “average driver” and therefore produces an optimalroute only for a driver of that type. However, generally differentdrivers evaluate route recommendations differently. For example,specific road types are preferred or avoided, or the average speed onexpressways varies depending on the type of vehicle, driver temperament,or the like. Taking into account this individual driver behavior orother preferences in the selection of the route (for example, preferringroutes that have more beautiful landscapes) is accomplished at presentlargely by a manual input of predetermined parameters, which cangenerally only happen when the vehicle is standing still. This reducesthe clarity and the ease of operation of the navigational systemespecially as the number of parameters rises, so that an appropriatelycalculated modification of all relevant parameters can only be expectedfrom experienced or technically aware users.

SUMMARY

An objective of the present invention is to make available anavigational system and a method for configuring a navigational systemof this type, which would make it possible to individually adjust userpreferences, without, as a result, burdening the user with cumbersomeinputting procedures.

In an example method for configuring a navigational system according tothe present invention, is vehicle, traffic, and/or driver data arerecorded by sensors, and individual behavior patterns and userpreferences are derived therefrom for the route planning. Accordingly, aconfiguring method of this type operates completely automatically on thebasis of information that is recorded by sensors, and the user of thenavigational system is not himself burdened with configuring the system.The term ‘patterns of behavior’, in this context, representssituation-specific regularities in the behavior of the driver, that arenarrowly limited with regard to behavior, for example, avoiding localthoroughfares of larger cities, preferring expressways, or the like. Inthe method according to the present invention, these types ofsignificant patterns of behavior of the user are recorded automaticallythrough data analysis and data statistics. The same applies to userpreferences, which on a higher level describe the partialities of theuser or the weighting of optimization criteria by the user of thenavigational system. In general, there exists a connection betweenpatterns of behavior and user preferences, such that the userpreferences combine and abstract commonalties that are contained invarious patterns of behavior. The patterns of behavior derived using thesensors therefore describe the typical user preferences in current orexpected driving situations, and they can be made available to thenavigational system for a route calculation and target tracking that areoriented specifically to the preferences of the user. In this context,the individual situation-specific user preferences may be stored asexplicit statistics in the database of the system and can be modifiedindependently of each other.

The patterns of behavior and/or the user preferences that are derivedfrom the sensor data, can be compared with the patterns of behaviorand/or user preferences that are preestablished in the navigationalsystem for the route planning. As a result of the comparison, it can beestablished whether the profile of user preferences that is active inthe navigational system is still correct, or whether such largedeviations exist that a change in the profile is necessary. A change ofthis type can either be carried out so as to result in a completely newuser profile that is generated on the basis of the sensor data, or froma quantity of user profiles that have been preestablished in thenavigational system the most appropriate profile can be selected.Examples for user preferences that can be automatically generated orpreestablished by the manufacturer are:

-   -   preferred road classes (expressway, local roads, city streets)    -   preferred road surroundings (for example, avoiding intersecting        streets)    -   driving velocity as related to road classes, assuming unhindered        traffic    -   route selection in response to traffic disruptions    -   stopping behavior of the driver (how long, how often, etc.)    -   route following in response to preestablished optimization        strategies (for example, the quickest route)    -   preferred means of transportation in intermodal route planning        (i.e., route planning using different means of locomotion, such        as automobile, train, local transportation systems, etc.).

In one refinement of the present invention, deviations of the user fromthe recommended route proposed by the navigational system can berecorded and used for adjusting the patterns of behavior and userpreferences. A system of this type is accordingly characterized by thefact that, in the course of the driver's use, it learns or adjustsitself to him. This adjustment takes place automatically, withoutburdening the user with cumbersome and technically difficult inputs.

In a further refinement of the present invention, the sensors determinechanges in the patterns of behavior of the user, and when changes ofthis type arise, the sensor detection of vehicle, traffic, and/or driverdata is changed or expanded, such that the parameters can be determinedthat most probably have caused the changes in the patterns of behavior.When a behavior of the user is detected that deviates from previouspatterns of behavior, a plausibility check is automatically initiated,which by sensor detection of additional parameters, attempts toestablish the reason for the current deviation in the pattern ofbehavior. Examples of significant detection features in a plausibilitycheck of this type are

-   -   the road class    -   local speed limits    -   the roadway condition, inter alia, described by degree of        wetness, wheel grip, roadway surface    -   the visibility conditions, inter alia, described by the        intensity of precipitation, density of fog, brightness    -   local traffic situation, inter alia, described by the vehichle's        velocity, the velocity, distance, and driving direction of        nearby vehicles in the vicinity of the original vehicle    -   time-critical arrival at destination, inter alia, described by        absolute time points or time ranges such as deadlines, opening        times    -   the purpose of the journey, if appropriate, to be derived from        the type of destination, such as business trip, shopping trip,        vacation trip    -   traffic and road condition information from official sources.

As soon as parameters of the aforementioned type have been recorded,they can be analyzed to determine those variables that, with a highdegree of probability, have caused the deviation in the pattern ofbehavior can be explained by the fact that the visibility conditions areextraordinarily bad. In this context, a variable is particularly to beconsidered a cause of the deviation if it has a value lying outside itsnormal range.

If the variables that are relevant for the deviation of the pattern ofbehavior have been successfully isolated, then existing patterns ofbehavior can be adjusted thereto or user-specific and situation-relevantnew patterns of behavior can be created. This pattern of behavior canthen be recorded in the database of the navigational system so as to betaken into account in future route planning.

In response to changing patterns of behavior in the database of thenavigational system, it is possible from time to time to carry out astatistical transformation of the patterns of behavior into userpreferences. This can occur especially by minimizing the significantdetection features and by largely avoiding local references, in order tobe able to apply the user preferences even in areas that are unknown tothe user.

The present invention also relates to a navigational system, which canpropose at least one route recommendation on the basis of preestablishedpatterns of behavior of the user and on the basis of user preferences.The navigational system is characterized in that a system for recordingand processing vehicle-, traffic-, and driver-related data is coupled tothe navigational system, and in that this system makes it possible tocarry out a configuration in accordance with a method of the typedescribed above. This means that the system is especially orientedtowards processing information that is detected by sensors, from whichinformation individual patterns of behavior and user preferences can bederived. Advantageously, using the system, deviations from the routerecommendation can also be detected, so that the navigational system canundertake an adaptation of the user preferences on the basis of thesedeviations. Furthermore, the system is flexible with respect to therecorded data, so that especially when deviations are established fromprevious patterns of behavior, the recorded parameters can be expandedsuch that the parameters that are relevant for the deviation in behaviorcan be detected, in the sense of a plausibility check.

The navigational system according to the present invention makespossible an automated preparation of user-specific andsituation-relevant preferences, which can be used for an individualroute planning. In this context, it is especially advantageous that nomanual input of the preferences via an input unit is required of theuser.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 schematically depicts the components of a navigational system.

FIG. 2 depicts an example embodiment according to the present inventionof the automatic user-profile detection in the navigational system inaccordance with FIG. 1.

FIG. 3 schematically depicts an entire system for navigation that ismodified according to the present invention.

DETAILED DESCRIPTION

In FIG. 1, the components of the navigational system are schematicallydepicted. The navigational system is composed of the subsystems, digitalroad map 100, computing module for determining the travel route 400,position determining system 300, system management 200, controllabledata-recording sensors 500, input unit 600, output unit 700, and targettracking system 900. A unit 1000 for communicating with other systemscan optionally also be present outside of the navigational system.

According to the present invention, the navigational system alsocontains a device 800 for automatically detecting individual userpreferences. Device 800, as depicted, can be a subsystem of thenavigational system, but it can also be operated separately from thenavigational system using its own data detection sensors and aninterface for making available patterns of behavior or user preferences.

A more detailed design of device 800 for detecting user profiles isdepicted in FIG. 2. Accordingly, this device 800 is made up of adetection unit 820, which controls the data to be recorded by sensors500 and digital map 100, position determining system 300, travel routedetermining system 400, and the input unit 600 (for user inputs that arepotentially required and context-related), preprocesses, this data (forexample, be compressing or deriving variables that are not directlymeasurable), and finally stores the data in raw database 810.

In unit 830 for data processing, the data from raw database 810 isstatistically processed, yielding patterns of behavior. From the latter,user-specific and situation-related user preferences are extracted fordetecting user preferences and are made available in database 850 foruse in the navigational system. Detection unit 820 can optionally alsoapply data that have been recorded by another system and were madeavailable via communication unit 1000 to subsystem 800.

Unit 840 is capable of detecting new patterns of behavior on the basisof comparing the features of previously recorded patterns of behaviorwith the currently detected features, and to automatically generate fromthe latter new user preferences and to store them in database 850.Alternatively, unit 840 can also check typical user preferences thathave been provided by the device manufacture for a typical averagedriver, on the basis of the recorded current and previous features indatabase 810, or on the basis of the automatically generated patterns ofbehavior with respect to their validity for the current user. Whensignificant deviations are ascertained, the active patterns of behaviorcan be adapted, or a more appropriate one can be selected from apreestablished repertoire of patterns of behavior.

New user preferences are generated, or preestablished user preferencesare adapted, only given the sufficient statistical reliability of thedata, for which an upper limit can be preestablished. The determinationis possible, for example, regarding the frequency of the patterns ofbehavior.

In FIG. 3, an overall system is depicted, in which at least parts ofdevice 800 are arranged self-sufficiently in mobile partial system 2000or in stationary partial system 3000, systems 2000 and 3000 beingconnected via a communications unit 1000.

The mode of functioning of the navigational system depicted in theFigures is represented below initially in the example of asituation-dependent driving velocity. Unit 840, on the basis of thenormally recorded detection features (for example, velocity, roadclass), detects that the driver on the expressway is traveling only at80 km/h instead of the typical driving speed of 130 km/h. The deviationfrom the typical driving velocity has therefore exceeded a specificlimit, it being possible, if appropriate, to preestablish this limit asuser-specific. After determining the described situation, detection unit820 controls sensors 500 such that additional detection features can berecorded and evaluated. These detection features can be, for example,determining the visibility, the road condition, and the local trafficsituation. In addition, detection unit 820 via communication unit 1000can query a data center with respect to additional traffic situationinformation for the relevant road area. The incoming data are stored inraw database 810 and are supplied to data evaluation unit 830.

In unit 830, the significant detection features are determined that areheld to be the cause for the deviation from the typical travel speeddetected by unit 840, and that probably describe a new driving situationor a new pattern of behavior. Thus, unit 830 can determine, for example,that the local traffic situation does not influence the vehicle (forexample, there are no vehicles traveling a short distance ahead at thesame speed), that the road condition is uncritical, but that thevisibility is limited by medium-intensity rain. One detection feature isgenerally isolated by unit 830 as relevant if it has a value thatdeviates from its standard range, which can be stored in a database.

Unit 840 now compares the current pattern of behavior with any alreadyexisting patterns of behavior and derives therefrom, for example, in thepresent case, a user preference according to the rule, “Given limitedvisibility as a result of medium to strong rainfall, the driver travelssignificantly slower on expressways than the typical driving speed.”Through the continuing adaptation of the user preferences, thisformulated preference can be still further generalized, for example,into the rule, “Given limited visibility, the road-class-typical travelvelocity is reduced by 30%.”

A further example of the mode of functioning of the system depicted inFIGS. 1 through 3 can be seen in the change of the route selectionbehavior of the navigational system. The initial situation in thisregard is, for example, that unit 840 detects and determines a deviationfrom the preestablished travel route recommendation, and that there area plurality of other similar patterns of behavior.

In this case, detection unit 820 therefore drives sensors 500 such thatadditional detection features, inter alia, can be recorded and evaluatedfor determining the visibility, the road conditions, and the localtraffic situation. In addition, detection unit 820 via communicationsunit 1000 can query a data center with respect to additional trafficcondition information for the relevant road area. In this context, itcan be a question of, for example, traffic disturbances that have notyet been taken into account in the travel route in forward segments. Theincoming data are stored in raw database 810 and are subsequentlysupplied to data evaluation unit 830.

In unit 830 for data evaluation, the significant detection features aredetermined that can be held to be a cause for the deviation, as detectedby unit 840, from the preestablished recommendation of the travel route,and that describe a new driving situation or a new pattern of behavior.Thus, unit 830 can determine, for example, that the additionalsituation-describing detection features do not have any specialqualities, but that the driver has selected a local detour.

Unit 840 for detecting user preferences compares this pattern ofbehavior with patterns of behavior that may already exist, and in thiscase, it derives therefrom, for example, a user preference in accordancewith the rule, “In medium-sized towns, local detours are preferred.” Theroute calculation can then apply this user preference such that thefollowing driving routes always avoid passing through medium-sizedtowns, even if the route is lengthened overall as a result.

The navigational system according to the present invention thereforepermits an automatic detection of individual user preferences withoutthe user of the system having to occupy himself therewith. The system,in the course of use, automatically adjusts itself to the user, so that,for example, a repeated traveling over specific road types at atypicalspeeds (for example, 80 km/h on expressways and 110 km/h on Federalhighways) automatically leads to recommending alternative routes havingroad classes on which more rapid routes can be determined under theaforementioned preconditions. Furthermore, a repeated, intentionalnon-following of the original target tracking instructions on a specificroute segment in the same way can ultimately lead to recommending acorrespondingly corrected driving route.

1. A method for configuring a navigational system, comprising:detecting, using sensors, at least one of vehicle data, traffic data,and driver data; deriving, from the at least one of the vehicle data,traffic data and driver data, at least one of individual patterns ofbehavior, situation specific patterns of behavior, location-independentpatterns of behavior and user preferences; comparing the derived atleast one of individual patterns of behavior, situation specificpatterns of behavior, location independent patterns of behavior, anduser preferences, to user preferences that are preestablished for routeplanning; detecting deviations from a route recommendation; adjustingthe user preferences based on the detected deviations; establishingchanges in patterns of behavior of the user; and when changes in thepatterns of behavior of the user arise, one of changing and expandingthe detection of the at least one of the vehicle data, traffic data, anddriver data such that the parameters are determined that likely causedthe changes in the pattern of behavior of the user.
 2. A navigationalsystem, comprising: sensors configured to detect at least one of vehicledata, traffic data and driver data; and a processor configured toderive, from the at least one of the vehicle data, traffic data anddriver data, at least one of patterns of behavior, situation specificpatterns of behavior, location independent patterns of behavior, anduser preferences, compare the derived at least one of individualpatterns of behavior, situation specific patterns of behavior, locationindependent patterns of behavior, and user preferences, to userpreferences that are preestablished for route planning, one of changingand expanding the detection of the at least one of the vehicle data,traffic data, and driver data such that the parameters are determinedthat likely caused the changes in the pattern of behavior of the userwhen changes in the patterns of behavior of the user arise, anddetermine a recommended route based on the at least one of the patternsof behavior, situation specific patterns of behavior, locationindependent patterns of behavior, and user preferences.